ENHANCING AYURVEDIC DIAGNOSTICS THROUGH MACHINE LEARNING VIA INTEGRTION OF SMART HEALTH MONITORING DATA

Abstract

Incorporating machine learning (ML) and smart health monitoring with Ayurvedic diagnostics is a novel approach to improving our traditional medical practices. To level up the recognition and categorization of the three Ayurvedic doshas—Pitta, Kapha, and Vata—the study uses an unsupervised learning approach to train health-related data from wearable devices like smartwatches and smartphones. With the help of useful data gathered from smart health devices—such as heart rate, physical activity, and sleep patterns—this study aims to improve the quality of Ayurvedic assessment and personalize treatment plans. This study took sleep data from the collected one to analyze the Prakriti. Using K-means clustering on sensored collected data, we are able to find patterns associated with it. This method makes use of unsupervised learning's capabilities to recognize undetectable patterns in datasets without predetermined labeling. The main dataset that we have utilized is the information gathered from smart devices that people usually wear and use to continuously monitor different physiological characteristics. After doing thorough preprocessing to guarantee the consistency and validity of our approach, EDA is performed to identify major attributes of Ayurvedic diagnosis. Based on collected information, the study's findings suggest that machine learning can distinguish between the three human constitutions with good accuracy; certain patterns are also categorized by clusters that are interrelated to the Ayurveda understanding of doshas. We have identified that by using machine learning methods, it is possible to identify the distinct patterns exhibited by each dosha. For example, those with a higher Vata characteristic do not sleep for longer periods and have more peaceful sleep patterns, and then some people with a higher Kapha characteristic. We offer a novel framework that combines Ayurveda and technology to produce a more comprehensive, data-driven approach to diagnosing health issues and providing quick resolution with the touch of tradition. This research has advanced the field. It draws attention to how ML may improve the precision and customization of Ayurvedic treatments, making them more usable and relevant in the current digital time. Also, in order to globalize subsequent research could also take an effort that points more on increasing the datasets with more demographic scope, with more number of parameters, with more health metrics, and also standardized dataset for enhancing the generalizability in this expanding field. Also, we can use other machine learning approaches that could further enhance this diagnostic process. In the conclusion it has been seen by extensive literature review and experiments that there is potential to reform the traditional healthcare field with the combination of conventional procedures, artificial intelligence, and smart health monitoring devices. As per Ayurvedic principles, this combination could result in improved health outcomes and a deeper understanding of individual health characteristics by offering a more precise, personalized, and preventive approach to health management.